Machine Learning and Medical Imaging presents state-of- the-art
machine learning methods in medical image analysis. It first
summarizes cutting-edge machine learning algorithms in medical
imaging, including not only classical probabilistic modeling and
learning methods, but also recent breakthroughs in deep learning,
sparse representation/coding, and big data hashing. In the second
part leading research groups around the world present a wide
spectrum of machine learning methods with application to different
medical imaging modalities, clinical domains, and organs. The
biomedical imaging modalities include ultrasound, magnetic
resonance imaging (MRI), computed tomography (CT), histology, and
microscopy images. The targeted organs span the lung, liver, brain,
and prostate, while there is also a treatment of examining genetic
associations. Machine Learning and Medical Imaging is an ideal
reference for medical imaging researchers, industry scientists and
engineers, advanced undergraduate and graduate students, and
clinicians.